Seshagiri S, Khalil H K
Scientific Research Lab, Ford Motor Company, Dearborn, MI 48121, USA.
IEEE Trans Neural Netw. 2000;11(1):69-79. doi: 10.1109/72.822511.
An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The effectiveness of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks.
提出了一种用于一类连续时间非线性对象输出跟踪的自适应输出反馈控制方案。采用径向基函数（RBF）神经网络对对象非线性进行自适应补偿。利用基于李雅普诺夫的设计方法对网络权值进行调整。该方法采用参数投影、控制饱和和高增益观测器来实现半全局一致最终有界性。通过仿真验证了所提方法的有效性。仿真还表明，通过将自适应控制与鲁棒控制相结合，可以容忍因使用低阶网络而产生的较大逼近误差。
